Inlämning av Examensarbete / Submission of Thesis

Zahoor Ali; Muhammad Qummer ul Arfeen MCS-2011:20, pp. 56. COM/School of Computing, 2011.

The work

Författare / Author: Zahoor Ali, Muhammad Qummer ul Arfeen
z_anthrops@yahoo.co.uk, qamarneo@hotmail.com
Titel / Title: The role of Machine Learning in Predicting CABG Surgery Duration
Abstrakt Abstract:

Context. Operating room (OR) is one of the most expensive resources of a hospital. Its mismanagement is associated with high costs and revenues. There are various factors which may cause OR mismanagement, one of them is wrong estimation of surgery duration. The surgeons underestimate or overestimate surgery duration which causes underutilization or overutilization of OR and medical staff. Resolving the issue of wrong estimate can result improvement of the overall OR planning.
Objectives. In this study we investigate two different techniques of feature selection, compare different regression based modeling techniques for surgery duration prediction. One of these techniques (with lowest mean absolute) is used for building a model. We further propose a framework for implementation of this model in the real world setup.
Results. In our case the selected technique (correlation based feature selection with best first search in backward direction) for feature selection could not produce better results than the expert’s opinion based approach for feature selection. Linear regression outperformed on both the data sets. Comparatively the mean absolute error of linear regression on experts’ opinion based data set was the lowest.
Conclusions. We have concluded that patterns exist for the relationship of the resultant prediction (surgery duration) and other important features related to patient characteristics. Thus, machine learning tools can be used for predicting surgery duration. We have also concluded that the proposed framework may be used as a decision support tool for facilitation in surgery duration prediction which can improve the planning of ORs and their resources.

Ämnesord / Subject: Datavetenskap - Computer Science\Artificial Intelligence
Datavetenskap - Computer Science\General
Nyckelord / Keywords: Machine learning, surgery duration prediction, operating room planning, data mining

Publication info

Dokument id / Document id: houn-8msdpv
Program:/ Programme Datavetenskapligt program/Computer Science
Registreringsdatum / Date of registration: 10/19/2011
Uppsatstyp / Type of thesis: Masterarbete/Master's Thesis (120 credits)

Context

Handledare / Supervisor: Dr. Marie Persson
marie.persson@bth.se
Examinator / Examiner: Dr. Lars Lundberg
Organisation / Organisation: Blekinge Institute of Technology
Institution / School: COM/School of Computing

+46 455 38 50 00
Anmärkningar / Comments:

Zahoor Ali
00923339474002

Muhammad Qummer ul Arfeen
0046760652203

Files & Access

Bifogad uppsats fil(er) / Files attached: bth2011ali.pdf (1369 kB, öppnas i nytt fönster)